A simple proportional-integral-derivative (PID) feedback controller is the most popular apparatus used in the industry for controlling the operation and performance of a process. A feedback controller is also known as a closed-loop controller.
Systems used for operating plants and monitoring the operation of one or more processes within such plants typically include several feedback (or closed-loop) PID controllers, hereinafter referred to as PID controller or simply controller, as standard “equipment” with assumed default values for the PID gains. In order for these plants, and processes therein, to operate correctly and robustly, each of the PID gains of the controller must be adequately and appropriately tuned for the application at hand. When the “best” PID gains are used, the controller will quickly react to overcome and compensate for any internally and/or externally induced disturbances to which the process is subjected. Examples of disturbances are: change in control set point, change in process characteristics, sensor noise and uncertainty, etc. However, determining the appropriate PID gains is a challenging task for engineers and plant operators because some level of user expertise is necessary for successfully establishing the “best” gains.
Several tools, methods, and theories are available for tuning PID controller gains (for example, Astrom and Hagglund, PID Controllers: Theory, Design, and Tuning, 2nd ed., ISA, 1995). However, in practice the bulk of these methods require a lot of engineering effort to get satisfactory results. Currently, control engineers use commercially available tools only as a starting point, and then “play” with the PID gains to get acceptable results. This is a very time consuming effort. Therefore, the notion of an auto-tuning or a self-tuning PID controller for determining PID gains with minimal operator interaction is highly desirable. This concept has tremendous commercial value, and there are a number of automatic gain tuners in the market. In some automatic gain tuners, the controller PID gains are derived analytically based on a low-order model of the process. In other methods, the tuning is based on the optimization of some performance measure of the controller as related to the characteristics of the frequency and/or time response of the process. Persons skilled in the art will recognize that current auto-tuning techniques require frequent adjustment of the PID gains, are unreliable, and are not particularly effective (Shinskey, Feedback Controllers for the Process Industries, McGraw Hill, 1994). Yet, the tuning of PID gains remains a subject of great practical interest because of the large number of PID controllers in existence, e.g., a typical refinery could have as many as 3,000 PID controllers.
In view of the foregoing, it is desirable to provide an improved method for tuning the controller gains. It is preferable for the gain tuner to require minimal operator interaction and for the tuning to be accomplished without the need for models of the process and/or the controller. It is further desirable to tune the PID controller gains while the controller continues to control the process.